OpenRaiser/Intern-Atlas huggingface dataset:
Towards Blockchain-based Multi-Agent Robotic Systems: Analysis, Classification and Applications
Venue: arXiv cs.RO 2019
Year: 2019

Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implementing Blockchain in a multi-robot network may be to increase the interaction efficiency between agents by providing more trusted information exchange, reaching a consensus in trustless conditions, assessing robot productivity or detecting performance problems, identifying intruders, allocating plans and tasks, deploying distributed solutions and joint missions. Blockchain-based applications are discussed to demonstrate how distributed ledger can be used to extend the number of research platforms and libraries for multi-agent robotic systems.
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
Venue: arXiv cs.LG 2019
Year: 2019

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to different operating conditions, fault modes, noise and equipment updates distribution shift exists across different data domains. This shift reduces the performance of predictive models previously built to specific conditions when no observed run-to-failure data is available for retraining. To address this issue, this paper proposes a new data-driven approach for domain adaptation in prognostics using Long Short-Term Neural Networks (LSTM). We use a time window approach to extract temporal information from time-series data in a source domain with observed RUL values and a target domain containing only sensor information. We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain. The experimental results show that the proposed method can provide more reliable RUL predictions under datasets with different operating conditions and fault modes. These results suggest that the proposed method offers a promising approach to performing domain adaptation in practical PHM applications.
Multi-Adapter RGBT Tracking
Venue: arXiv cs.CV 2019
Year: 2019

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.
A Link Between the Multiplicative and Additive Functional Asplund's Metrics
Venue: arXiv cs.CV 2019
Year: 2019

Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided probing in the Logarithmic Image Processing (LIP) framework. Two metrics were defined, namely the LIP-multiplicative Asplund's metric which is robust to variations of object thickness (or opacity) and the LIP-additive Asplund's metric which is robust to variations of camera exposure-time (or light intensity). Maps of distances-i.e. maps of these metric values-were also computed between a reference template and an image. Recently, it was proven that the map of LIP-multiplicative As-plund's distances corresponds to mathematical morphology operations. In this paper, the link between both metrics and between their corresponding distance maps will be demonstrated. It will be shown that the map of LIP-additive Asplund's distances of an image can be computed from the map of the LIP-multiplicative Asplund's distance of a transform of this image and vice-versa. Both maps will be related by the LIP isomorphism which will allow to pass from the image space of the LIP-additive distance map to the positive real function space of the LIP-multiplicative distance map. Experiments will illustrate this relation and the robustness of the LIP-additive Asplund's metric to lighting changes.
Convergence and Hardness of Strategic Schelling Segregation
Venue: arXiv cs.GT 2019
Year: 2019

The phenomenon of residential segregation was captured by Schelling's famous segregation model where two types of agents are placed on a grid and an agent is content with her location if the fraction of her neighbors which have the same type as her is at least $τ$, for some $0<τ<1$. Discontent agents simply swap their location with a randomly chosen other discontent agent or jump to a random empty cell. We analyze a generalized game-theoretic model of Schelling segregation which allows more than two agent types and more general underlying graphs modeling the residential area. For this we show that both aspects heavily influence the dynamic properties and the tractability of finding an optimal placement. We map the boundary of when improving response dynamics (IRD), i.e., the natural approach for finding equilibrium states, are guaranteed to converge. For this we prove several sharp threshold results where guaranteed IRD convergence suddenly turns into the strongest possible non-convergence result: a violation of weak acyclicity. In particular, we show such threshold results also for Schelling's original model, which is in contrast to the standard assumption in many empirical papers. Furthermore, we show that in case of convergence, IRD find an equilibrium in $\mathcal{O}(m)$ steps, where $m$ is the number of edges in the underlying graph and show that this bound is met in empirical simulations starting from random initial agent placements.
Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes
Venue: arXiv cs.RO 2019
Year: 2019

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time.
A substitute for the classical Neumann--Morgenstern characteristic function in cooperative differential games
Venue: arXiv cs.GT 2019
Year: 2019

In this paper, we present a systematic overview of different endogenous optimization-based characteristic functions and discuss their properties. Furthermore, we define and analyze in detail a new, $η$-characteristic function. This characteristic function has a substantial advantage over other characteristic functions in that it can be obtained with a minimal computational effort and has a reasonable economic interpretation. In particular, the new characteristic function can be seen as a reduced version of the classical Neumann-Morgenstern characteristic function, where the players both from the coalition and from the complementary coalition use their previously computed strategies instead of solving respective optimization problems. Our finding are illustrated by a pollution control game with $n$ non-identical players. For the considered game, we compute all characteristic functions and compare their properties. Quite surprisingly, it turns out that both the characteristic functions and the resulting cooperative solutions satisfy some symmetry relations.
Inductive Analysis of the Internet Protocol TLS
Venue: arXiv cs.CR 2019
Year: 2019

Internet browsers use security protocols to protect sensitive messages. An inductive analysis of TLS (a descendant of SSL 3.0) has been performed using the theorem prover Isabelle. Proofs are based on higher-order logic and make no assumptions concerning beliefs or finiteness. All the obvious security goals can be proved; session resumption appears to be secure even if old session keys have been compromised. The proofs suggest minor changes to simplify the analysis. TLS, even at an abstract level, is much more complicated than most protocols that researchers have verified. Session keys are negotiated rather than distributed, and the protocol has many optional parts. Nevertheless, the resources needed to verify TLS are modest: six man-weeks of effort and three minutes of processor time.
Shallow Embedding of Type Theory is Morally Correct
Venue: arXiv cs.LO 2019
Year: 2019

There are multiple ways to formalise the metatheory of type theory. For some purposes, it is enough to consider specific models of a type theory, but sometimes it is necessary to refer to the syntax, for example in proofs of canonicity and normalisation. One option is to embed the syntax deeply, by using inductive definitions in a proof assistant. However, in this case the handling of definitional equalities becomes technically challenging. Alternatively, we can reuse conversion checking in the metatheory by shallowly embedding the object theory. In this paper, we consider the standard model of a type theoretic object theory in Agda. This model has the property that all of its equalities hold definitionally, and we can use it as a shallow embedding by building expressions from the components of this model. However, if we are to reason soundly about the syntax with this setup, we must ensure that distinguishable syntactic constructs do not become provably equal when shallowly embedded. First, we prove that shallow embedding is injective up to definitional equality, by modelling the embedding as a syntactic translation targeting the metatheory. Second, we use an implementation hiding trick to disallow illegal propositional equality proofs and constructions which do not come from the syntax. We showcase our technique with very short formalisations of canonicity and parametricity for Martin-Löf type theory. Our technique only requires features which are available in all major proof assistants based on dependent type theory.
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Venue: arXiv cs.SI 2019
Year: 2019

Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos. In this paper, we develop a novel content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by exploring the comments from the audience who watched the video. Different from existing solutions, OVCP does not directly analyze the content of the video and its pre-click information (e.g., title and thumbnail). Therefore, it is robust against sophisticated content creators who often generate clickbait videos that can bypass the current clickbait detectors. We evaluate OVCP with a real-world dataset collected from YouTube. Experimental results demonstrate that OVCP is effective in identifying clickbait videos and significantly outperforms both state-of-the-art baseline models and human annotators.
Deep Multi-View Learning via Task-Optimal CCA
Venue: arXiv cs.LG 2019
Year: 2019

Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CCA projection for discrimination and the CCA projection itself, or they are linear only. We address these deficiencies by simultaneously optimizing a CCA-based and a task objective in an end-to-end manner. Together, these two objectives learn a non-linear CCA projection to a shared latent space that is highly correlated and discriminative. Our method shows a significant improvement over previous state-of-the-art (including deep supervised approaches) for cross-view classification, regularization with a second view, and semi-supervised learning on real data.
A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure
Venue: arXiv cs.CY 2019
Year: 2019

A considerable part of recent research in smart cities and IoT has focused on achieving energy savings in buildings and supporting aspects related to sustainability. In this context, the educational community is one of the most important ones to consider, since school buildings constitute a large part of non-residential buildings, while also educating students on sustainability matters is an investment for the future. In this work, we discuss a methodology for achieving energy savings in schools based on the utilization of data produced by an IoT infrastructure installed inside school buildings and related educational scenarios. We present the steps comprising this methodology in detail, along with a set of tangible results achieved within the GAIA project. We also showcase how an IoT infrastructure can support activities in an educational setting and produce concrete outcomes, with typical levels of 20% energy savings.
Pakistan's Internet Voting Experiment
Venue: arXiv cs.CY 2019
Year: 2019

Pakistan recently conducted small-scale trials of a remote Internet voting system for overseas citizens. In this contribution, we report on the experience: we document the unique combination of sociopolitical, legal, and institutional factors motivating this exercise. We describe the system and it's reported vulnerabilities, and we also highlight new issues pertaining to materiality. If this system is deployed in the next general elections, as seems likely, this development would constitute the largest enfranchised diaspora in the world. Our goal in this paper, therefore, is to provide comprehensive insight into Pakistan's experiment with Internet voting, emphasize outstanding challenges, and identify directions for future research.
Flatter is better: Percentile Transformations for Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

It is well known that explicit user ratings in recommender systems are biased towards high ratings, and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users' distributions. In this work, we demonstrate that lack of \textit{flatness} in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show a smoothed version of this transformation designed to yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments show improved ranking performance for these percentile transformations with state-of-the-art recommendation algorithms in four real-world data sets.
CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers
Venue: arXiv cs.AR 2019
Year: 2019

Memory controller scheduling is crucial in multicore processors, where DRAM bandwidth is shared. Since increased number of requests from multiple cores of processors becomes a source of bottleneck, scheduling the requests efficiently is necessary to utilize all the computing power these processors offer. However, current multicore processors are using traditional memory controllers, which are designed for single-core processors. They are unable to adapt to changing characteristics of memory workloads that run simultaneously on multiple cores. Existing schedulers may disrupt locality and bank parallelism among data requests coming from different cores. Hence, novel memory controllers that consider and adapt to the memory access characteristics, and share memory resources efficiently and fairly are necessary. We introduce Core-Aware Dynamic Scheduler (CADS) for multicore memory controller. CADS uses Reinforcement Learning (RL) to alter its scheduling strategy dynamically at runtime. Our scheduler utilizes locality among data requests from multiple cores and exploits parallelism in accessing multiple banks of DRAM. CADS is also able to share the DRAM while guaranteeing fairness to all cores accessing memory. Using CADS policy, we achieve 20% better cycles per instruction (CPI) in running memory intensive and compute intensive PARSEC parallel benchmarks simultaneously, and 16% better CPI with SPEC 2006 benchmarks.
Efficient computation of the Jacobi symbol
Venue: arXiv cs.DS 2019
Year: 2019

The family of left-to-right GCD algorithms reduces input numbers by repeatedly subtracting the smaller number, or multiple of the smaller number, from the larger number. This paper describes how to extend any such algorithm to compute the Jacobi symbol, using a single table lookup per reduction. For both quadratic time GCD algorithms (Euclid, Lehmer) and subquadratic algorithms (Knuth, Schönhage, Möller), the additional cost is linear, roughly one table lookup per quotient in the quotient sequence. This method was used for the 2010 rewrite of the Jacobi symbol computation in GMP.
Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
Venue: arXiv cs.LG 2019
Year: 2019

Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data. A line of research stemming from semi-supervised learning uses pseudo labeling to directly generate "pseudo labels" for the unlabeled target data and trains a classifier on the now-labeled target data, where the samples are selected or weighted based on some measure of confidence. In this paper, we propose multi-purposing the discriminator to not only aid in producing domain-invariant representations but also to provide pseudo labeling confidence.
Search-Based Motion Planning for Performance Autonomous Driving
Venue: arXiv cs.RO 2019
Year: 2019

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.
Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G
Venue: arXiv cs.IT 2019
Year: 2019

Mobile Network Operators (MNOs) are in process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in 5G and Beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top 5 challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond-5G and 6G.
Diving Deeper into Underwater Image Enhancement: A Survey
Venue: arXiv cs.CV 2019
Year: 2019

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time, and hence; a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before. To be specific, we first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented including network architecture, network parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.